This file is part of the supplementary material of the manuscript: Didino, D., Brandtner, M., & Knops, A. (2021). No influence of masked priming on the multiplication fact retrieval in a result verification task.

This script run the data pre-processing of experiment 3.

Dataset loaded: exp_soa_2_data.csv (data of result verification task)

Load libraries and data:

library('tidyverse')
#> -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
#> v ggplot2 3.3.5     v purrr   0.3.4
#> v tibble  3.1.0     v dplyr   1.0.5
#> v tidyr   1.1.3     v stringr 1.4.0
#> v readr   1.4.0     v forcats 0.5.1
#> -- Conflicts ------------------------------------------ tidyverse_conflicts() --
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag()    masks stats::lag()
library('here')
#> here() starts at D:/mult_prime
library('janitor')
#> 
#> Attaching package: 'janitor'
#> The following objects are masked from 'package:stats':
#> 
#>     chisq.test, fisher.test
library('plotly')
#> 
#> Attaching package: 'plotly'
#> The following object is masked from 'package:ggplot2':
#> 
#>     last_plot
#> The following object is masked from 'package:stats':
#> 
#>     filter
#> The following object is masked from 'package:graphics':
#> 
#>     layout
# library('knitr')
library('kableExtra')
#> 
#> Attaching package: 'kableExtra'
#> The following object is masked from 'package:dplyr':
#> 
#>     group_rows

# Load my functions
source(here('funcs', 'load_my_functions.R'))

# Load data (included )
exp3_rowData <- read_csv(here('data', 'rawdata', 'exp_soa_2_data.csv'))
#> 
#> -- Column specification --------------------------------------------------------
#> cols(
#>   sj = col_character(),
#>   age = col_double(),
#>   gender = col_character(),
#>   op1 = col_double(),
#>   op2 = col_double(),
#>   probe = col_double(),
#>   product = col_double(),
#>   prime = col_double(),
#>   filler = col_double(),
#>   probe_type = col_character(),
#>   prime_type = col_character(),
#>   prime_decade_consistency = col_character(),
#>   prime_cond = col_character(),
#>   SOA = col_character(),
#>   acc = col_double(),
#>   RT = col_double(),
#>   block = col_double(),
#>   problem_size = col_character(),
#>   timing = col_character()
#> )

Evaluate accuracy

Plot accuracy by subject

plot_acc_sj <-
  exp3_rowData %>%
  group_by(sj) %>%
  summarise(acc = mean(acc)) %>%
  ggplot(aes(x = acc, y = sj)) +
  geom_point() +
  coord_cartesian(xlim = c(0, 1)) +
  geom_vline(xintercept = 0.75,
             linetype = 'dashed',
             color = 'red',
             size = 1)

ggplotly(plot_acc_sj)

Plot accuracy by block and subject

plot_acc_block_sj <- 
  exp3_rowData %>% 
  group_by(sj, block) %>% 
  summarise(acc = mean(acc)) %>% 
  ungroup() %>% 
  ggplot(aes(x = block, y = acc, group = sj, colour = sj)) +
  geom_line() +
  coord_cartesian(ylim = c(0, 1)) +
  scale_x_discrete(limits = factor(1:10)) +
  geom_hline(yintercept = 0.75,
             linetype = 'dashed',
             color = 'red',
             size = 1)
#> `summarise()` has grouped output by 'sj'. You can override using the `.groups` argument.

ggplotly(plot_acc_block_sj)

Evaluate RTs

Plot RTs by subject

plot_RT_sj <-
  exp3_rowData %>% 
  filter(acc == 1) %>% 
  group_by(sj) %>%
  ggplot(aes(x = sj, y = RT)) +
  geom_violin(draw_quantiles = c(0.25, 0.5, 0.75))

ggplotly(plot_RT_sj)

Plot RT by block and subject

plot_RTs_block_sj <- 
  exp3_rowData %>% 
  filter(acc == 1) %>% 
  group_by(sj, block) %>% 
  summarise(RT = mean(RT)) %>% 
  ggplot(aes(x = block, y = RT, group = sj, colour = sj)) +
  geom_line() +
  scale_x_discrete(limits = factor(1:10))
#> `summarise()` has grouped output by 'sj'. You can override using the `.groups` argument.

ggplotly(plot_RTs_block_sj)

Select participants and trials

Excluded participants:

exp3_excludedSJ <- 
  exp3_rowData %>% 
  filter(!(sj %in% c('sj27')))

Removed trials:

# filler: 0 = experimental trial, 1 = filler trial
exp3_no_filler <- 
  exp3_excludedSJ %>% 
  filter(filler == 0 & probe_type == 'product')

Problems selected for the analysis:

exp3_no_filler %>% 
  select('op1', 'op2') %>% 
  table %>%
  make_table('Problems selected')
Problems selected
3 4 5 6 7 8
3 0 0 435 435 435 0
4 0 870 435 0 435 435
5 435 435 870 435 435 435
6 435 0 435 870 435 435
7 435 435 435 435 870 0
8 0 435 435 435 0 0

Accuracy by conditions

Table with accuracy mean, standard deviation and standard error (aggregated on subject and condition)

# Calculate statistics for accuracy
exp3_stats_acc <- 
  exp3_no_filler %>%
  return_stats(c('SOA', 'problem_size', 'prime_cond'), DV = 'acc')
#> `summarise()` has grouped output by 'sj', 'SOA', 'problem_size'. You can override using the `.groups` argument.
#> `summarise()` has grouped output by 'SOA', 'problem_size'. You can override using the `.groups` argument.

exp3_stats_acc %>% 
   make_table('Accuracy by problem_size and prime_cond')
Accuracy by problem_size and prime_cond
SOA problem_size prime_cond N_sj Mean SD SE
SOA_050 large neigh_con 29 0.94 0.07 0.01
SOA_050 large neigh_inc 29 0.92 0.09 0.02
SOA_050 large neutral 29 0.92 0.10 0.02
SOA_050 large unrel_con 29 0.94 0.07 0.01
SOA_050 large unrel_inc 29 0.93 0.08 0.01
SOA_050 small neigh_con 29 0.95 0.06 0.01
SOA_050 small neigh_inc 29 0.95 0.06 0.01
SOA_050 small neutral 29 0.96 0.05 0.01
SOA_050 small unrel_con 29 0.97 0.05 0.01
SOA_050 small unrel_inc 29 0.96 0.06 0.01
SOA_170 large neigh_con 29 0.92 0.10 0.02
SOA_170 large neigh_inc 29 0.90 0.08 0.02
SOA_170 large neutral 29 0.93 0.09 0.02
SOA_170 large unrel_con 29 0.92 0.11 0.02
SOA_170 large unrel_inc 29 0.92 0.11 0.02
SOA_170 small neigh_con 29 0.96 0.06 0.01
SOA_170 small neigh_inc 29 0.97 0.04 0.01
SOA_170 small neutral 29 0.96 0.05 0.01
SOA_170 small unrel_con 29 0.97 0.05 0.01
SOA_170 small unrel_inc 29 0.97 0.04 0.01
SOA_220 large neigh_con 29 0.94 0.07 0.01
SOA_220 large neigh_inc 29 0.94 0.07 0.01
SOA_220 large neutral 29 0.93 0.08 0.01
SOA_220 large unrel_con 29 0.92 0.09 0.02
SOA_220 large unrel_inc 29 0.93 0.10 0.02
SOA_220 small neigh_con 29 0.95 0.06 0.01
SOA_220 small neigh_inc 29 0.95 0.06 0.01
SOA_220 small neutral 29 0.97 0.04 0.01
SOA_220 small unrel_con 29 0.96 0.05 0.01
SOA_220 small unrel_inc 29 0.94 0.06 0.01

Tables with demographics and performance

Tables are created with the my function return_info()

tb <- 
  return_info(exp3_no_filler)
tb$gender
Gender
gender n percent
female 18 62.07
male 11 37.93 
tb$age
Age
mean sd min max
27.55 6.26 18 39 
tb$error
Percentege incorrect response
N_error N_trial percent_error
728 13050 5.58 
tb$omitted
Percentege omitted responses
N_omitted N_trial percent_omitted
80 13050 0.61 
tb$timing
Percentege wrong timing (wrong stimuli onset/offset)
N_wrong_timing N_trial percent_wrong_timing
3 13050 0.02 
tb$less_200
Percentege RT < 200 ms
N_less_200 N_trial percent_less_200
11 13050 0.08

Exclude trials

Exclude trials with omitted answer, errors, RT less than 200 ms, and wrong timing (wrong stimuli onset/offset)

# Exclude omitted, errors, RT < 200 ms, and wrong timing
exp3_correct_ans <- 
  exp3_no_filler %>% 
  filter((acc == 1) & (RT > 200) & (timing == 'ok'))

Outliers

For each participant, trials more than 2.5 standard deviations from the mean are classified as outliers and excluded from the frequentist analysis.

# identify outlier

threshold = 2.5

exp3_rt_outlier <- 
  exp3_correct_ans %>%
  group_by(sj) %>% 
  mutate(outlier = !between(
    RT,
    mean(RT) - (threshold * sd(RT)),
    mean(RT) + (threshold * sd(RT))
  )) %>% 
  ungroup()


# percentage outliers

exp3_rt_outlier %>% 
  summarise(
    N_outlier = sum(outlier == TRUE),
    N_trial = length(outlier)
  ) %>% 
  mutate(percent_outlier = N_outlier / N_trial * 100) %>% 
  make_table('Outliers')
Outliers
N_outlier N_trial percent_outlier
399 12314 3.24

Plot the trials classified as outliers

plot_outlier <- 
  ggplot(data = exp3_rt_outlier) +
  geom_boxplot(aes(x = sj, y = RT)) +
  geom_point(aes(x = sj, y = RT, colour =  outlier),
             position = position_dodge(0.9, preserve = 'total'),
             shape = 20,
             size = 0.5) +
  theme(axis.text.x = element_text(angle = 50, vjust = 1, hjust = 1))

ggplotly(plot_outlier)

Save processed data

The processed data are saved in the file data/processed/exp3_data.rds.

saveRDS(
  object = exp3_rt_outlier,
  file = here('data', 'processed', 'exp3_data.rds'))

session information

xfun::session_info()
#> R version 4.0.5 (2021-03-31)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 19044)
#> 
#> Locale:
#>   LC_COLLATE=English_United Kingdom.1252 
#>   LC_CTYPE=English_United Kingdom.1252   
#>   LC_MONETARY=English_United Kingdom.1252
#>   LC_NUMERIC=C                           
#>   LC_TIME=English_United Kingdom.1252    
#> 
#> Package version:
#>   askpass_1.1         assertthat_0.2.1    backports_1.2.1    
#>   base64enc_0.1.3     BH_1.75.0.0         blob_1.2.1         
#>   broom_0.7.6         bslib_0.2.4         callr_3.6.0        
#>   cellranger_1.1.0    cli_2.4.0           clipr_0.7.1        
#>   colorspace_2.0-0    compiler_4.0.5      cpp11_0.2.7        
#>   crayon_1.4.1        crosstalk_1.1.1     curl_4.3           
#>   data.table_1.14.0   DBI_1.1.1           dbplyr_2.1.1       
#>   digest_0.6.27       dplyr_1.0.5         dtplyr_1.1.0       
#>   ellipsis_0.3.1      evaluate_0.14       fansi_0.4.2        
#>   farver_2.1.0        forcats_0.5.1       fs_1.5.0           
#>   gargle_1.1.0        generics_0.1.0      ggplot2_3.3.5      
#>   glue_1.4.2          googledrive_1.0.1   googlesheets4_0.3.0
#>   graphics_4.0.5      grDevices_4.0.5     grid_4.0.5         
#>   gtable_0.3.0        haven_2.3.1         here_1.0.1         
#>   highr_0.8           hms_1.0.0           htmltools_0.5.1.1  
#>   htmlwidgets_1.5.3   httr_1.4.2          ids_1.0.1          
#>   isoband_0.2.4       janitor_2.1.0       jquerylib_0.1.3    
#>   jsonlite_1.7.2      kableExtra_1.3.4    knitr_1.33         
#>   labeling_0.4.2      later_1.1.0.1       lattice_0.20.41    
#>   lazyeval_0.2.2      lifecycle_1.0.0     lubridate_1.7.10   
#>   magrittr_2.0.1      markdown_1.1        MASS_7.3.53.1      
#>   Matrix_1.3.2        methods_4.0.5       mgcv_1.8.34        
#>   mime_0.10           modelr_0.1.8        munsell_0.5.0      
#>   nlme_3.1.152        openssl_1.4.3       pillar_1.6.0       
#>   pkgconfig_2.0.3     plotly_4.9.3        prettyunits_1.1.1  
#>   processx_3.5.1      progress_1.2.2      promises_1.2.0.1   
#>   ps_1.6.0            purrr_0.3.4         R6_2.5.0           
#>   rappdirs_0.3.3      RColorBrewer_1.1.2  Rcpp_1.0.6         
#>   readr_1.4.0         readxl_1.3.1        rematch_1.0.1      
#>   rematch2_2.1.2      reprex_2.0.0        rlang_0.4.10       
#>   rmarkdown_2.7       rprojroot_2.0.2     rstudioapi_0.13    
#>   rvest_1.0.0         sass_0.3.1          scales_1.1.1       
#>   selectr_0.4.2       snakecase_0.11.0    splines_4.0.5      
#>   stats_4.0.5         stringi_1.5.3       stringr_1.4.0      
#>   svglite_2.0.0       sys_3.4             systemfonts_1.0.2  
#>   tibble_3.1.0        tidyr_1.1.3         tidyselect_1.1.0   
#>   tidyverse_1.3.1     tinytex_0.31        tools_4.0.5        
#>   utf8_1.2.1          utils_4.0.5         uuid_0.1.4         
#>   vctrs_0.3.7         viridisLite_0.3.0   webshot_0.5.2      
#>   withr_2.4.1         xfun_0.22           xml2_1.3.2         
#>   yaml_2.2.1